A text image (raster image of a text) received by scanning or photographing a document usually has a large quantity of artifacts and noise, which are visible during reading from a screen and printing with large resolution. The noise can be, for example, surface noise (separate noise pixels along character outlines). The similar distortions are intrinsic not only for images with text information, but also for images with graphical content (scheme, graphics, diagrams, and/or others synthetic images) as well.
There are a number of solutions for improving visual perception of a raster document image. For example, the text in a document can be recognized, and a font which is most close to an original can be selected. Unfortunately, it is not always possible to precisely fit the font, and the errors in recognition can lead to erroneous character replacing. Moreover, character recognition requires significant time and computing capability. For these reasons, character recognition is not a practical solution if only visual text improvement is required.
Another possible solution is a vectorization of a raster image. Vectorization is a complex and computationally expensive process. Further, vectorization does not ensure that the document saved in vector form will not have a larger size and/or will not include significant artifacts.
One more simple approach is using a method of image filtering. Existing methods usually do not yield a good enough result when they are applied to an image of a text. Various methods of local processing wherein improving images is based on neighboring pixel values cannot provide sufficient results.
Thus, there is a need for a document image enhancement method that utilizes special approaches that are not sufficiently developed in the areas of image processing methods and/or computer graphics.